Robustness with fminimax
6 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
Hello everyone,
I have to study robustness of my system, so my variables are uncertain and I have to add another variable to my objective function and it becomes:
original objective-function: myfun = @(x) sum (x(1:n)./polyval(p3,x(1:n)));
robust optimization : min max myfun = @(x) sum ([x(1:n) + r(1:n)]./polyval(p3,[x(1:n) + r(1:n)])); min for "x" and max for "r" .
"r" is the perturbation so it is very small,
my problem is non-convex and with non-linear constraints, and I solve the original problem with fmincon and it provides a good optimum.
For the robust optimization, I think that I can solve it by the fminimax, but I don't know how formulate it ??
Thanks,
0 commentaires
Réponses (2)
Sargondjani
le 31 Mai 2012
they way you present the problem now, you could just replace x(1:n) with y(1:n)=x(1:n) + r(1:n) in the orginal problem....
if instead you want to get solutions for every r then you could looop through r:
myfun=@(x,r)....
r=...
for ir=1:length(r)
my_fun_ir=@(x)myfun(x,r(ir))
%solve minimax where you store every solution as x(ir), for example
end
i hope this helps...
0 commentaires
Voir également
Catégories
En savoir plus sur Multiobjective Optimization dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!